Measurably evolving pathogens in the genomic era

Abstract

Current sequencing technologies have created unprecedented opportunities for studying microbial populations. For pathogens with comparatively low per-site mutation rates, such as DNA viruses and bacteria, whole-genome sequencing can reveal the accumulation of novel genetic variation between population samples taken at different times. The concept of 'measurably evolving populations' and related analytical approaches have provided powerful insights for fast-evolving RNA viruses, but their application to other pathogens is still in its infancy. We argue that previous distinctions between slow- and fast-evolving pathogens become blurred once evolution is assessed at a genome-wide scale, and we highlight important analytical challenges to be overcome to infer pathogen population dynamics from genomic data.

Keywords: DNA virus; bacteria; epidemiological models; evolutionary rate; infectious disease; phylodynamics.

Figure 1

There is a broad negative relationship between evolutionary rate and genome size across a range of different viruses and bacteria. Evolutionary rates shown are based on a representative selection of published datasets of heterochronously sampled, complete or partial genomes sampled between one and six decades apart. See Supplementary Table S1 for details on rate estimates.

Figure 2

The relative time scales of epidemiological and evolutionary processes (at the whole genome level) can vary widely among viral and bacterial pathogens. Average intervals between transmission and nucleotide substitution events were calculated as the reciprocal of the pathogen's reported generation time and estimated evolutionary rate, respectively. Evolutionary rates were estimated based on published datasets of heterochronous genomes sampled up to two decades apart. Axes are on a log scale but due to considerable uncertainties and heterogeneities associated with the underlying parameters, are only labeled with broad temporal units. For pathogens above the unity line, novel genetic variation is expected to become fixed faster than the average time between host-to-host transmission events, making it possible in principle to reconstruct individual transmission pathways from genomic data; the same is not true for pathogens below the unity line. The lower end of the evolutionary time scale is ultimately bounded by the underlying mutation rate per genome replication event, as indicated by the blunt left-hand sides of the clouds representing parameter estimates. See Supplementary Table S1 for details on rate estimates.

Figure 3

Consistent with a general pattern for measurably evolving populations, the evolutionary rates of microbial pathogens decrease as a function of the time span over which they are estimated. Data shown are selected representative examples, including one group of RNA viruses (primate lentiviruses SIV and HIV in blue, taken from [25], genomic rates extrapolated from pol gene sequences) and several bacterial pathogens. See Supplementary Table S1 for details.